treatment effects , unobserved heterogeneity , overidentification test , instrumental variables , generated regressors , wild bootstrap , teenage pregnancies , network analysis , stochastic block model , latent position model , centrality
Abstract:
My dissertation investigates commonly used testing and estimation procedures and extends
these by taking into account more heterogeneity. In chapter 2, me and my co-author Andreas
Dzemski provide a new overidentification test that allows for essential heterogeneity. In chapter
3, I prove weak consistency up to a measure preserving transformation for maximum-likelihood
estimation of unobserved latent positions in a Euclidean space just based on observable information
of the agent's linking behavior. In chapter 4, I propose a new measure of centrality
which exploits the latent space structure and identifies agents who connect clusters.
Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt.
![[img]](https://madoc-dev.bib.uni-mannheim.de/style/images/fileicons/application_pdf.png)
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